add argument to switch 8bit

eval_configs/minigpt4_eval.yaml

@@ -5,6 +5,7 @@ model:
   freeze_qformer: True
   max_txt_len: 160
   end_sym: "###"
+  low_resource: True
   prompt_path: "prompts/alignment.txt"
   prompt_template: '###Human: {} ###Assistant: '
   ckpt: '/path/to/pretrained/ckpt/'

minigpt4/models/mini_gpt4.py

@@ -36,11 +36,13 @@ def __init__(
         prompt_path="",
         prompt_template="",
         max_txt_len=32,
+        low_resource=False,  # use 8 bit and put vit in cpu
         end_sym='\n',
     ):
         super().__init__()
 
         self.tokenizer = self.init_tokenizer()
+        self.low_resource = low_resource
 
         print('Loading VIT')
         self.visual_encoder, self.ln_vision = self.init_vision_encoder(
@@ -83,10 +85,19 @@ def __init__(
         self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False)
         self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
 
-        self.llama_model = LlamaForCausalLM.from_pretrained(
-            llama_model, torch_dtype=torch.float16,
-            load_in_8bit=True, device_map="auto"
-        )
+        if self.low_resource:
+            self.llama_model = LlamaForCausalLM.from_pretrained(
+                llama_model,
+                torch_dtype=torch.float16,
+                load_in_8bit=True,
+                device_map="auto"
+            )
+        else:
+            self.llama_model = LlamaForCausalLM.from_pretrained(
+                llama_model,
+                torch_dtype=torch.float16,
+            )
+
         for name, param in self.llama_model.named_parameters():
             param.requires_grad = False
         print('Loading LLAMA Done')
@@ -107,18 +118,22 @@ def __init__(
         else:
             self.prompt_list = []
 
-    def encode_img(self, image):
-        device = image.device
+    def vit_to_cpu(self):
         self.ln_vision.to("cpu")
         self.ln_vision.float()
         self.visual_encoder.to("cpu")
         self.visual_encoder.float()
-        image = image.to("cpu")
 
-        image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
-        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+    def encode_img(self, image):
+        device = image.device
+        if self.low_resource:
+            self.vit_to_cpu()
+            image = image.to("cpu")
 
         with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
+            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+
             query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
             query_output = self.Qformer.bert(
                 query_embeds=query_tokens,
@@ -216,6 +231,7 @@ def from_config(cls, cfg):
         vit_precision = cfg.get("vit_precision", "fp16")
         freeze_vit = cfg.get("freeze_vit", True)
         freeze_qformer = cfg.get("freeze_qformer", True)
+        low_resource = cfg.get("low_resource", False)
 
         prompt_path = cfg.get("prompt_path", "")
         prompt_template = cfg.get("prompt_template", "")
@@ -236,6 +252,7 @@ def from_config(cls, cfg):
             prompt_path=prompt_path,
             prompt_template=prompt_template,
             max_txt_len=max_txt_len,
+            low_resource=low_resource,
             end_sym=end_sym
         )